Objective: The purpose of this study was to understand federal workplace injury/illness trends. Methods: Over 1.5 million federal and Postal Service employee workers' compensation (WC) claims from 2007 to 2022 were linked to employment data and analyzed. Results: From 2007 to 2019, Falls, Slips, Trips represented the highest proportion of claims (30.7%), followed by Overexertion and Bodily Reaction (24.4%), Unclassified (16.4%), Contact with Objects and Equipment (13.1%), Violence and Other Injuries by Persons or Animals (8.8%), Transportation Incidents (4.0%), Exposure to Harmful Substances or Environments (2.5%), and Fires and Explosions (0.24%). From 2020 to 2022, COVID-19 drove a major shift to Exposure to Harmful Substances or Environments representing the highest proportion of claims (44.3%). Conclusions: Claims data represent a potentially rich data source that employing agencies can use to focus prevention and treatment of injury/illness.

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2(nd) most accurate model measured by WIS in 2021-22 and the 5(th) most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Background: Diseases and conditions related to the respiratory system contribute to work-related morbidity, mortality, and disability. Details on the causes and nature of work-related respiratory disease and the specific industries in which they occur are limited. This study identifies respiratory-related claims in the Ohio Bureau of Workers’ Compensation (OHBWC) system and describes claim and worker characteristics to inform public health surveillance. Methods: We developed a list of respiratory-related International Classification of Diseases Clinical Modification (ICD-CM) diagnosis codes and searched over 2 million claims filed between 2001 and 2018 in the OHBWC system for at least one of these codes. The claim characteristics, rates of claims by employer industry classification, and causes of claims from narrative text were determined for these respiratory-related claims. Results: Among the 23,015 respiratory-related claims (5.8 per 10,000 full-time equivalents [FTE]), 54.6% had at least one ICD-CM code for Allergic Reactions and 30.6% had at least one code for Toxic Effects of Substances Chiefly Non-medicinal as to Source. Claim causes from narrative text included Chemical Exposure (30.3%), Activity Suggesting Exposure (24.4%), and Vapors, Gases, Dusts, or Fumes (VGDF) Exposure (19.3%). The highest overall rates of respiratory-related claims among private employers were for the agriculture, forestry & fishing (11.4 per 10,000), public safety (ambulance services) (11.3), and manufacturing (10.7) industry sectors. Conclusions: Respiratory-related claims in the OHBWC system were often acute in nature and included allergic reactions. Narratives from these claims provide insight into the work-related exposures and events causing claims or the disease and symptom factors surrounding claims. © 2024

OBJECTIVE: This study analyzed Ohio workers' compensation (WC) data to identify potential prevention strategies for common Oil and Gas Extraction industry claims. METHODS: Claim rates for 2001-2018 were calculated per full-time equivalent employee. Descriptive analyses on free-text descriptions of lost-time (LT) claims (>7 days away from work) identified common characteristics among claims and injured workers. RESULTS: Among 3,134 claims, 860 (27%) were LT. The industry group, Drilling contractors, experienced the highest LT claims rate, while the cost from Servicing contractors was highest. Contact with objects and equipment caused the highest LT claims rate. The most frequent LT occupation was Roustabout and LT work activity was Material Handling. Transportation Incidents caused most fatalities and hospitalizations. Over half of LT claims were from short-tenured workers. CONCLUSIONS: Both proven and innovative approaches are needed to reduce severe WC claims in this industry.

NIOSH researchers are pioneering the study of musculoskeletal health as professional ergonomists. We examine physical and social components of work environments to mitigate musculoskeletal injury risks. Part of our mission is to reduce the burden of work-related musculoskeletal disorders (MSDs) through a focused program of research and prevention that protects workers from MSDs, helps management mitigate related risks and liabilities, and helps practitioners improve the efficacy of workplace interventions. The purpose of this discussion panel is to disseminate research findings and recommendations (1) to practitioners to interpret and apply the results of research to real-world problems, and (2) to inspire researchers to continue their efforts to protect the millions of workers at risk. © 2023 Human Factors and Ergonomics Society.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

Background: This study examined age-group differences in the rate, severity, and cost of injuries among construction workers to support evidence-based worker safety and health interventions in the construction industry. Methods: Ohio workers' compensation claims for construction workers were used to estimate claim rates and costs by age group. We analyzed claims data auto-coded into five event/exposure categories: transportation incidents; slips, trips, and falls (STFs); exposure to harmful substances and environments (EHS); contact with objects and equipment (COB); and overexertion and bodily reaction. American Community Survey data were used to determine the proportion of workers by age group. Results: From 2007-2017, among 72,416 accepted injury claims for 166,000 construction full-time equivalent (FTE) per year, nearly half were caused by COB, followed by STFs (20%) and overexertion (20%). Claim rates related to COB and EHS were highest among those 18-24 years old, with claim rates of 313.5 and 25.9 per 10,000 FTE, respectively. STFs increased with age, with the highest claim rates for those 55-64 years old (94.2 claims per 10,000 FTE). Overexertion claim rates increased and then declined with age, with the highest claim rate for those 35-44 years old (87.3 per 10,000 FTE). While younger workers had higher injury rates, older workers had higher proportions of lost-time claims and costs per claim. The total cost per FTE was highest for those 45-54 years old ($1,122 per FTE). Conclusion: The variation in rates of injury types by age suggests age-specific prevention strategies may be useful.

Problem: Compared to other industries, construction workers have higher risks for serious fall injuries. This study describes the burden and circumstances surrounding injuries related to compensable slip, trip, and fall (STF) claims from private construction industries covered by the Ohio Bureau of Workers Compensation. Methods: STF injury claims in the Ohio construction industry from 20102017 were manually reviewed. Claims were classified as: slips or trips without a fall (STWOF), falls on the same level (FSL), falls to a lower level (FLL), and other. Claim narratives were categorized by work-related risk and contributing factors. Demographic, employer, and injury characteristics were examined by fall type and claim type (medical-only (MO, 07 days away from work, DAFW) or lost-time (LT, 8 DAFW)). Claim rates per 10,000 estimated full-time equivalent employees (FTEs) were calculated. Results: 9,517 Ohio construction industry STF claims occurred during the 8-year period, with an average annual rate of 75 claims per 10,000 FTEs. The rate of STFs decreased by 37% from 2010 to 2017. About half of the claims were FLL (51%), 29% were FSL, 17% were STWOF, and 3% were other. Nearly 40% of all STF claims were LT; mostly among males (96%). The top three contributing factors for STWOF and FSL were: slip/trip hazards, floor irregularities, and ice/snow; and ladders, vehicles, and stairs/steps for FLL. FLL injury rates per 10,000 FTE were highest in these industries: Foundation, Structure, and Building Exterior Contractors (52); Building Finishing Contractors (45); and Residential Building Construction (45). The highest rate of FLL LT claims occurred in the smallest firms, and the FLL rate decreased as construction firm size increased. Discussion and Practical Applications: STF rates declined over time, yet remain common, requiring prevention activities. Safety professionals should focus on contributing factors when developing prevention strategies, especially high-risk subsectors and small firms. 2023

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44

As emerging and re-emerging infectious diseases like dengue, Ebola, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Standard methods for assessing autochthonous (local) transmission risk make either indirect estimates based on ecological suitability or direct estimates only after local cases accumulate. However, an overlooked source of epidemiological data that can meaningfully inform risk assessments prior to outbreak emergence is the absence of transmission by imported cases. Here, we present a method for updating a priori ecological estimates of transmission risk using real-time importation data. We demonstrate our method using Zika importation and transmission data from Texas in 2016, a high-risk region in the southern United States. Our updated risk estimates are lower than previously reported, with only six counties in Texas likely to sustain a Zika epidemic, and consistent with the number of autochthonous cases detected in 2017. Importation events can thereby provide critical, early insight into local transmission risks as infectious diseases expand their global reach.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Problem: Compared to other industries, construction workers have higher risks for serious fall injuries. This study describes the burden and circumstances surrounding injuries related to compensable slip, trip, and fall (STF) claims from private construction industries covered by the Ohio Bureau of Workers' Compensation. Methods: STF injury claims in the Ohio construction industry from 2010-2017 were manually reviewed. Claims were classified as: slips or trips without a fall (STWOF), falls on the same level (FSL), falls to a lower level (FLL), and other. Claim narratives were categorized by work-related risk and contributing factors. Demographic, employer, and injury characteristics were examined by fall type and claim type (medical-only (MO, 0-7 days away from work, DAFW) or lost-time (LT, ≥8 DAFW)). Claim rates per 10,000 estimated full-time equivalent employees (FTEs) were calculated. Results: 9,517 Ohio construction industry STF claims occurred during the 8-year period, with an average annual rate of 75 claims per 10,000 FTEs. The rate of STFs decreased by 37% from 2010 to 2017. About half of the claims were FLL (51%), 29% were FSL, 17% were STWOF, and 3% were "other." Nearly 40% of all STF claims were LT; mostly among males (96%). The top three contributing factors for STWOF and FSL were: slip/trip hazards, floor irregularities, and ice/snow; and ladders, vehicles, and stairs/steps for FLL. FLL injury rates per 10,000 FTE were highest in these industries: Foundation, Structure, and Building Exterior Contractors (52); Building Finishing Contractors (45); and Residential Building Construction (45). The highest rate of FLL LT claims occurred in the smallest firms, and the FLL rate decreased as construction firm size increased. Discussion and Practical Applications: STF rates declined over time, yet remain common, requiring prevention activities. Safety professionals should focus on contributing factors when developing prevention strategies, especially high-risk subsectors and small firms.

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Observer, manual single-frame video, and automated computer vision measures of the Hand Activity Level (HAL) were compared. HAL can be measured three ways: 1) observer rating (HAL(O)), 2) calculated from single-frame multimedia video task analysis for measuring frequency (F) and duty cycle (D) (HAL(F)), or 3) from automated computer vision (HAL(C)). This study analyzed videos collected from three prospective cohort studies to ascertain HAL(O), HAL(F), and HAL(C) for 419 industrial videos. Although the differences for the three methods were relatively small on average (<1), they were statistically significant (p<.001). A difference between the HAL(C) and HAL(F) ratings within 1 point on the HAL scale was the most consistent, where more than two thirds (68%) of all the cases were within that range and had a linear regression through the mean coefficient of 1.03 (R(2) =0.89). The results suggest that the computer vision methodology yields comparable results as single-frame video analysis. | The ACGIH Hand Activity Level (HAL) was obtained for 419 industrial tasks using three methods: observation, calculated using single-frame video analysis and computer vision. The computer vision methodology produced results that were comparable to single-frame video analysis. | eng

INTRODUCTION: Adverse childhood experiences and overdose are linked in a cycle that affects individuals and communities across generations. The Centers for Disease Control and Prevention's Overdose Data to Action cooperative agreement supports a comprehensive public health approach to overdose prevention and response activities across the U.S. Exposure to traumatic events during childhood can increase the risk for myriad health outcomes, including overdose; therefore, many Overdose Data to Action recipients leveraged funds to address adverse childhood experiences. METHODS: In 2021, an inventory of Overdose Data to Actionfunded activities implemented in 2019 and 2020 showed that 34 of the 66 recipients proposed overdose prevention activities that support people who have experienced adverse childhood experiences or that focus on preventing the intergenerational transmission of adverse childhood experiences. Activities were coded by adverse childhood experience prevention strategy, level of the social ecology, and whether they focused on neonatal abstinence syndrome. RESULTS: Most activities among Overdose Data to Action recipients occurred at the community level of the socialecologic model and under the intervene to lessen harms adverse childhood experience prevention strategy. Of the 84 adverse childhood experiencerelated activities taking place across 34 jurisdictions, 44 are focused on neonatal abstinence syndrome. CONCLUSIONS: Study results highlight the opportunities to expand the breadth of adverse childhood experience prevention strategies across the social ecology. Implementing cross-cutting overdose and adverse childhood experiencerelated activities that span the socialecologic model are critical for population-level change and have the potential for the broadest impact. Focusing on neonatal abstinence syndrome also offers a unique intervention opportunity for both adverse childhood experience and overdose prevention.

SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

BACKGROUND: Although recent studies have identified important risk factors associated with incident carpal tunnel syndrome (CTS), risk factors associated with its severity have not been well explored. OBJECTIVE: To examine the associations between personal, workplace psychosocial and biomechanical factors and incident work disability among workers with CTS. METHODS: Between 2001 and 2010 five research groups conducted coordinated prospective studies of CTS and related work disability among US workers from various industries. Workers with prevalent or incident CTS (N=372) were followed for up to 6.4 years. Incident work disability was measured as: (1) change in work pace or work quality, (2) lost time or (3) job change following the development of CTS. Psychosocial factors were assessed by questionnaire. Biomechanical exposures were assessed by observation and measurements and included force, repetition, duty cycle and posture. HRs were estimated using Cox models. RESULTS: Disability incidence rates per 100 person-years were 33.2 for changes in work pace or quality, 16.3 for lost time and 20.0 for job change. There was a near doubling of risk for job change among those in the upper tertile of the Hand Activity Level Scale (HR 2.17; 95% CI 1.17 to 4.01), total repetition rate (HR 1.75; 95% CI 1.02 to 3.02), % time spent in all hand exertions (HR 2.20; 95% CI 1.21 to 4.01) and a sixfold increase for high job strain. Sensitivity analyses indicated attenuation due to inclusion of the prevalent CTS cases. CONCLUSION: Personal, biomechanical and psychosocial job factors predicted CTS-related disability. Results suggest that prevention of severe disability requires a reduction of both biomechanical and organisational work stressors.

Disparities in antiretroviral treatment (ART) access by race for men who have sex with men (MSM) with HIV persist. We assessed whether race-based medical mistrust and HIV stigma impact ART adherence among MSM with HIV. Longitudinal data were drawn from a RCT of a messaging intervention to promote sexual health among MSM. Regression models tested associations between baseline race-based medical mistrust, HIV stigma, and ART adherence at follow-up. In multivariable models with the overall sample of MSM with HIV (n = 383), baseline medical mistrust was negatively associated with ART adherence 3-months post-baseline. Among participants of color (i.e., Black/African American, Hispanic/Latino, or another race; n = 301), HIV stigma was negatively associated with optimal ART adherence 6-months post-baseline. Medical mistrust was longitudinally associated with reduced ART adherence among racially and ethnically diverse MSM with HIV. HIV-related services might prioritize patients reporting medical mistrust for additional supports.

Introduction: This study analyzed workers compensation (WC) claims among private employers insured by the Ohio state-based WC carrier to identify high-risk industries by detailed cause of injury. Methods: A machine learning algorithm was used to code each claim by U.S. Bureau of Labor Statistics (BLS) event/exposure. The codes assigned to lost-time (LT) claims with lower algorithm probabilities of accurate classification or those LT claims with high costs were manually reviewed. WC data were linked with the state's unemployment insurance (UI) data to identify the employer's industry and number of employees. BLS data on hours worked per employee were used to estimate full-time equivalents (FTE) and calculate rates of WC claims per 100 FTE. Results: 140,780 LT claims and 633,373 medical-only claims were analyzed. Although counts and rates of LT WC claims declined from 2007 to 2017, the shares of leading LT injury event/exposures remained largely unchanged. LT claims due to Overexertion and Bodily Reaction (33.0%) were most common, followed by Falls, Slips, and Trips (31.4%), Contact with Objects and Equipment (22.5%), Transportation Incidents (7.0%), Exposure to Harmful Substances or Environments (2.8%), Violence and Other Injuries by Persons or Animals (2.5%), and Fires and Explosions (0.4%). These findings are consistent with other reported data. The proportions of injury event/exposures varied by industry, and high-risk industries were identified. Conclusions: Injuries have been reduced, but prevention challenges remain in certain industries. Available evidence on intervention effectiveness was summarized and mapped to the analysis results to demonstrate how the results can guide prevention efforts. Practical Applications: Employers, safety/health practitioners, researchers, WC insurers, and bureaus can use these data and machine learning methods to understand industry differences in the level and mix of risks, as well as industry trends, and to tailor safety, health, and disability prevention services and research. 2021

OBJECTIVE: This prospective study assessed the risk of developing rotator cuff syndrome (RCS) with separate or specific combinations of biomechanical exposures measures, controlling for individual confounders. BACKGROUND: Compared with other musculoskeletal disorders, rates of work-related shoulder musculoskeletal disorders have been declining more slowly. METHOD: We conducted up to 2 years of individual, annual assessments of covariates, exposures, and health outcomes for 393 U.S. manufacturing and healthcare workers without RCS at baseline. Task-level biomechanical exposures assessed exposure to forceful exertions (level, exertion rates, duty cycles), vibration, and upper arm postures (flexion, abduction). Hazard ratios (HRs) were calculated with Cox proportional hazard models. RESULTS: We observed 39 incident RCS cases in 694 person-years (incidence rate = 5.62 per 100 person-years). Adjusting for confounders, we found increased risk of incident RCS associated with forceful hand exertions per minute for three upper arm posture tertiles: flexion ≥45° (≥28.2% time, HR = 1.11, CI [1.01, 1.22]), abduction ≥30° (11.9-21.2%-time, HR = 1.18, CI [1.04, 1.34]), and abduction >60° (≥4.8% time, HR = 1.16, CI [1.04, 1.29]). We failed to observe statistically significant effects for other interactions or any separate measures of biomechanical exposure. CONCLUSION: This study highlights the importance of assessing combinations of exposure to forceful repetition and upper arm elevation when developing interventions for preventing RCS. APPLICATION: Based on these results, interventions that reduce exposure to forceful repetition (i.e., lower force levels and/or slower exertion rates) may reduce the risk of RCS, especially when upper arm elevation cannot be avoided.

Overexertion is a leading cause of work-related musculoskeletal disorders (WMSDs) among construction workers. Nearly 90% of construction jobs require manual handling of materials for approximately one half of the worker's time (1). In 2015, overexertion from lifting and lowering materials caused 30% of WMSDs among construction workers; overexertion involving pushing, pulling, holding, carrying, and catching materials caused an additional 37% of WMSDs (1). This study examined the rate and cost of WMSD claims from overexertion among Ohio construction workers during 2007-2017. Workers' compensation claims related to overexertion that were submitted to the Ohio Bureau of Worker's Compensation (OHBWC) by workers in the construction industry for injuries and illnesses occurring during 2007-2017 were analyzed. Rates and costs of allowed claims were measured by age group. Workers aged 35-44 years experienced the highest claim rate: 63 per 10,000 full-time employees (FTEs) for WMSDs from overexertion. However, claims by workers aged 45-54 years and 55-64 years were more costly on average and resulted in more days away from work. Ergonomic design improvements and interventions are needed to ensure that the majority of construction workers can safely perform jobs throughout their careers. Age-specific WMSD prevention and risk communication efforts also might be helpful.

BACKGROUND: In South Sudan, the civil war in 2016 led to mass displacement in Juba that rapidly spread to other regions of the country. Access to health care was limited because of attacks against health facilities and workers and pregnant women and newborns were among the most vulnerable. Translation of newborn guidelines into public health practice, particularly during periods of on-going violence, are not well studied during humanitarian emergencies. During 2016 to 2017, we assessed the delivery of a package of community- and facility-based newborn health interventions in displaced person camps to understand implementation outcomes. This case analysis describes the challenges encountered and mitigating strategies employed during the conduct of an original research study. DISCUSSION: Challenges unique to conducting research in South Sudan included violent attacks against humanitarian aid workers that required research partners to modify study plans on an ongoing basis to ensure staff and patient safety. South Sudan faced devastating cholera and measles outbreaks that shifted programmatic priorities. Costs associated with traveling study staff and transporting equipment kept rising due to hyperinflation and, after the July 2016 violence, the study team was unable to convene in Juba for some months to conduct refresher trainings or monitor data collection. Strategies used to address these challenges were: collaborating with non-research partners to identify operational solutions; maintaining a locally-based study team; maintaining flexible budgets and timelines; using mobile data collection to conduct timely data entry and remote quality checks; and utilizing a cascade approach for training field staff. CONCLUSIONS: The case analysis provides lessons that are applicable to other humanitarian settings including the need for flexible research methods, budgets and timelines; innovative training and supervision; and a local research team with careful consideration of sociopolitical factors that impact their access and safety. Engagement of national and local stakeholders can ensure health services and data collection continue and findings translate to public health action, even in contexts facing severe and unpredictable insecurity.

Numerous recent assessments indicate that meat and poultry processing facility workers are at increased risk for infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1-4). Physical proximity to other workers and shared equipment can facilitate disease transmission in these settings (2-4). The disproportionate number of foreign-born workers employed in meat and poultry processing reflects structural, social, and economic inequities that likely contribute to an increased COVID-19 incidence in this population* (5). In May 2020, the Maryland Department of Health and CDC investigated factors that might affect person-to-person SARS-CoV-2 transmission among persons who worked at two poultry processing facilities. A survey administered to 359 workers identified differences in risk factors for SARS-CoV-2 infection between workers born outside the United States and U.S.-born workers. Compared with U.S.-born workers, foreign-born workers had higher odds of working in fixed locations on the production floor (odds ratio [OR] for cutup and packaging jobs = 4.8), of having shared commutes (OR = 1.9), and of living with other poultry workers (OR = 6.0). They had lower odds of participating in social gatherings (OR for visits to family = 0.2; OR for visits to friends = 0.4), and they visited fewer businesses in the week before the survey than did their U.S.-born coworkers. Some workplace risk factors can be mitigated through engineering and administrative controls focused on the production floor, and this will be of particular benefit to the foreign-born workers concentrated in these areas. Employers and health departments can also partner with local organizations to disseminate culturally and linguistically tailored messages about risk reduction behaviors in community settings, including shared transportation(§) and household members dwelling in close quarters.

BACKGROUND: The purpose of this analysis was to identify and prioritize high-risk industry groups for traumatic brain injury (TBI) prevention efforts. METHODS: Workers with TBI from 2001 to 2011 were identified from the Ohio Bureau of Workers' Compensation data. To prioritize industry groups by claim type (lost-time (>/=8 days away from work) and total claims) and injury event categories, we used a prevention index (PI) that averaged TBI counts and rate ranks (PI = (count rank + rate rank)/2). TBI rates per 10 000 estimated full-time equivalent (FTE = 2000 h/y) workers were calculated. RESULTS: From 2001 to 2011, 12 891 TBIs were identified among private employers, resulting in a rate of 5.1 TBIs per 10 000 FTEs. Of these, 40% (n = 5171) were lost-time TBIs, at a rate of 2.0 per 10 000 FTEs. Spectator Sports had the highest lost-time TBI rate (13.5 per 10 000 FTEs), whereas General Freight Trucking had the greatest number of lost-time TBIs (n = 293). Based on PIs, General Freight Trucking ranked first for lost-time TBIs for all injury events combined. Several industry groups within Construction, General and Specialized Freight Trucking, Services to Building and Dwellings, Employment Services, and Restaurants and Other Eating Places ranked high across multiple injury event categories for lost-time TBIs. CONCLUSIONS: The high-ranking industry groups identified from our study can be used to effectively direct occupational TBI prevention efforts.

Recent studies have shown the 2001 American Conference of Governmental Industrial Hygienists (ACGIH((R))) Threshold Limit Value (TLV((R))) for Hand Activity was not sufficiently protective for workers at risk of carpal tunnel syndrome (CTS). These studies led to a revision of the TLV and Action Limit. This study compares the effect of applying the 2018 TLV vs. the 2001 TLV to predict incident CTS within a large occupational pooled cohort study (n = 4,321 workers). Time from study enrollment to first occurrence of CTS was modeled using Cox proportional hazard regression. Adjusted and unadjusted hazard ratios for incident CTS were calculated using three exposure categories: below the Action Limit, between the Action Limit and TLV, and above the TLV. Workers exposed above the 2001 Action Limit demonstrated significant excess risk of carpal tunnel syndrome, while the 2018 TLV demonstrated significant excess risk only above the TLV. Of 186 total cases of CTS, 52 cases occurred among workers exposed above the 2001 TLV vs. 100 among those exposed above the 2018 value. Eliminating exposures above the 2001 TLV might have prevented 11.2% of all cases of CTS seen in our pooled cohort, vs. 25.1% of cases potentially prevented by keeping exposures below the 2018 value. The 2018 revision of the TLV better protects workers from CTS, a recognized occupational health indicator important to public health. A significant number of workers are currently exposed to forceful repetitive hand activity above these guidelines. Public health professionals should promulgate these new guidelines and encourage employers to reduce hand intensive exposures to prevent CTS and other musculoskeletal disorders.

As emerging and re-emerging infectious arboviruses like dengue, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Indirect estimates of risk from vector habitat suitability maps are prone to great uncertainty, while direct estimates from epidemiological data are only possible after cases accumulate and, given environmental constraints on arbovirus transmission, cannot be widely generalized beyond the focal region. Combining these complementary methods, we use disease importation and transmission data to improve the accuracy and precision of a priori ecological risk estimates. We demonstrate this approach by estimating the spatiotemporal risks of Zika virus transmission throughout Texas, a high-risk region in the southern United States. Our estimates are, on average, 80% lower than published ecological estimates-with only six of 254 Texas counties deemed capable of sustaining a Zika epidemic-and they are consistent with the number of autochthonous cases detected in 2017. Importantly our method provides a framework for model comparison, as our mechanistic understanding of arbovirus transmission continues to improve. Real-time updating of prior risk estimates as importations and outbreaks arise can thereby provide critical, early insight into local transmission risks as emerging arboviruses expand their global reach.

OBJECTIVE: The aim of this study was to describe levels of integration between occupational safety and health (OSH) and workplace wellness programs/practices/policies ("programs") among participants in an insurer-sponsored wellness grant program. METHODS: We analyzed survey responses about year 1 of an insurer-sponsored grant to start a wellness program from 220 small- and medium-sized employers. Responses yielded 25 indicators of OSH-wellness integration, and 10 additional indicators to summarize multiple responses. RESULTS: At least half of the employers (N = 220) reported some level of integration within five of seven categories of OSH-wellness integration. Employers sometimes considered ergonomics, safety, or substance exposure hazards while designing their wellness program (15%) or reduced such hazards to support their wellness program (24%). Few meaningful differences were observed by employer size. CONCLUSION: Although high levels of integration were unusual, some degree of integration was common for most indicator categories.

BACKGROUND: Ambulance service workers frequently transfer and transport patients. These tasks involve occupational injury risks such as heavy lifting, awkward postures, and frequent motor vehicle travel. METHODS: We examined Ohio workers' compensation injury claims among state-insured ambulance service workers working for private employers from 2001 to 2011. Injury claim counts and rates are presented by claim types, diagnoses, and injury events; only counts are available by worker characteristics. RESULTS: We analyzed a total of 5882 claims. The majority were medical-only (<8 days away from work). The overall injury claim rate for medical-only and lost-time cases was 12.1 per 100 full-time equivalents. Sprains and strains accounted for 60% of all injury claims. Overexertion from patient handling was the leading injury event, followed by motor vehicle roadway incidents. CONCLUSIONS: Study results can guide the development or improvement of injury prevention strategies. Focused efforts related to patient handling and vehicle incidents are needed.